{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:01:50.199019Z",
     "start_time": "2025-05-15T08:01:50.195616Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.metrics import silhouette_score"
   ],
   "id": "bdf20596656fa6fd",
   "outputs": [],
   "execution_count": 34
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:02:28.763522Z",
     "start_time": "2025-05-15T08:02:28.759081Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 直接定义西瓜数据集4.0\n",
    "data = np.array([\n",
    "    [0.697, 0.460], [0.774, 0.376], [0.634, 0.264], [0.608, 0.318],\n",
    "    [0.556, 0.215], [0.403, 0.237], [0.481, 0.149], [0.437, 0.211],\n",
    "    [0.666, 0.091], [0.243, 0.267], [0.245, 0.057], [0.343, 0.099],\n",
    "    [0.639, 0.161], [0.657, 0.198], [0.360, 0.370], [0.593, 0.042],\n",
    "    [0.719, 0.103], [0.359, 0.188], [0.339, 0.241], [0.282, 0.257],\n",
    "    [0.748, 0.232], [0.714, 0.346], [0.483, 0.312], [0.478, 0.437],\n",
    "    [0.525, 0.369], [0.751, 0.489], [0.532, 0.472], [0.473, 0.376],\n",
    "    [0.725, 0.445], [0.446, 0.459]\n",
    "])"
   ],
   "id": "358de9bd1e7f1e7",
   "outputs": [],
   "execution_count": 39
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:02:32.937499Z",
     "start_time": "2025-05-15T08:02:32.932732Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 数据标准化\n",
    "scaler = StandardScaler()\n",
    "data_scaled = scaler.fit_transform(data)"
   ],
   "id": "582010a1abd560fd",
   "outputs": [],
   "execution_count": 40
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:09:05.301383Z",
     "start_time": "2025-05-15T08:09:05.294635Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# K-means算法\n",
    "class KMeans:\n",
    "    def __init__(self, k, max_iter=100):\n",
    "        self.k = k\n",
    "        self.max_iter = max_iter\n",
    "        self.centers = None\n",
    "\n",
    "    def fit(self, data, init_centers):\n",
    "        self.centers = init_centers\n",
    "        for _ in range(self.max_iter):\n",
    "            clusters = [[] for _ in range(self.k)]\n",
    "            for x in data:\n",
    "                distances = np.linalg.norm(x - self.centers, axis=1)\n",
    "                cluster_index = np.argmin(distances)\n",
    "                clusters[cluster_index].append(x)\n",
    "            new_centers = []\n",
    "            for cluster in clusters:\n",
    "                if len(cluster) > 0:\n",
    "                    new_centers.append(np.mean(cluster, axis=0))\n",
    "                else:\n",
    "                    new_centers.append(self.centers[np.random.choice(self.k)])  # 随机选择一个中心点\n",
    "            new_centers = np.array(new_centers)\n",
    "            if np.all(self.centers == new_centers):\n",
    "                break\n",
    "            self.centers = new_centers\n",
    "        return clusters\n",
    "\n",
    "    def predict(self, data):\n",
    "        predictions = []\n",
    "        for x in data:\n",
    "            distances = np.linalg.norm(x - self.centers, axis=1)\n",
    "            cluster_index = np.argmin(distances)\n",
    "            predictions.append(cluster_index)\n",
    "        return predictions\n",
    "\n",
    "    def sse(self, data, clusters):\n",
    "        sse_value = 0\n",
    "        for i, cluster in enumerate(clusters):\n",
    "            if len(cluster) > 0:\n",
    "                cluster_data = np.array(cluster)\n",
    "                sse_value += np.sum(np.linalg.norm(cluster_data - self.centers[i], axis=1) ** 2)\n",
    "        return sse_value"
   ],
   "id": "c96d7a3fa7475160",
   "outputs": [],
   "execution_count": 50
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:09:16.269788Z",
     "start_time": "2025-05-15T08:09:16.266550Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 设置三组不同的K值和初始中心点\n",
    "k_values = [2, 3, 4]\n",
    "init_centers_list = [\n",
    "    np.array([[0.697, 0.460], [0.774, 0.376]]),\n",
    "    np.array([[0.634, 0.264], [0.608, 0.318], [0.556, 0.215]]),\n",
    "    np.array([[0.403, 0.237], [0.481, 0.149], [0.437, 0.211], [0.666, 0.091]])\n",
    "]"
   ],
   "id": "9dca502cb4d9689e",
   "outputs": [],
   "execution_count": 51
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-05-15T08:09:28.827561Z",
     "start_time": "2025-05-15T08:09:28.472811Z"
    }
   },
   "cell_type": "code",
   "source": [
    "# 实验比较\n",
    "for i, k in enumerate(k_values):\n",
    "    init_centers = init_centers_list[i]\n",
    "    kmeans = KMeans(k)\n",
    "    clusters = kmeans.fit(data_scaled, init_centers)\n",
    "    predictions = kmeans.predict(data_scaled)\n",
    "    sse_value = kmeans.sse(data_scaled, clusters)\n",
    "    silhouette_avg = silhouette_score(data_scaled, predictions)\n",
    "    print(f\"K={k}, 初始中心点：{init_centers}\")\n",
    "    print(\"聚类结果：\")\n",
    "    for j, cluster in enumerate(clusters):\n",
    "        print(f\"类别{j + 1}：{cluster}\")\n",
    "    print(\"预测结果：\")\n",
    "    print(predictions)\n",
    "    print(f\"SSE: {sse_value}\")\n",
    "    print(f\"轮廓系数: {silhouette_avg}\")\n",
    "    # 可视化\n",
    "    plt.figure()\n",
    "    for j, cluster in enumerate(clusters):\n",
    "        if len(cluster) > 0:\n",
    "            cluster_data = np.array(cluster)\n",
    "            plt.scatter(cluster_data[:, 0], cluster_data[:, 1], label=f\"类别{j + 1}\")\n",
    "    plt.scatter(kmeans.centers[:, 0], kmeans.centers[:, 1], c='red', marker='x', label=\"中心点\")\n",
    "    plt.title(f\"K={k}\")\n",
    "    plt.legend()\n",
    "    plt.show()"
   ],
   "id": "877dcc7fa1adedc1",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=2, 初始中心点：[[0.697 0.46 ]\n",
      " [0.774 0.376]]\n",
      "聚类结果：\n",
      "类别1：[array([1.04951638, 1.42735344]), array([0.48907463, 0.33353699]), array([-0.80183051, -0.29040057]), array([-0.58772917, -0.49067682]), array([-1.80936623, -0.05931258]), array([-1.79677204, -1.67692846]), array([-1.07260574,  0.73408949]), array([-1.07890284, -0.66784427]), array([-1.2048448 , -0.25958883]), array([-1.5637794 , -0.13634191]), array([-0.29806265,  0.28731939]), array([-0.32954814,  1.25018599]), array([-0.03358452,  0.72638656]), array([1.38955968, 1.65073849]), array([0.01049516, 1.51978863]), array([-0.36103363,  0.78030709]), array([1.22583513, 1.31180945]), array([-0.53105529,  1.41965051])]\n",
      "类别2：[array([1.53439294, 0.78030709]), array([ 0.65279919, -0.08242138]), array([ 0.16162552, -0.45986509]), array([-0.31065685, -0.96825865]), array([ 0.85430633, -1.41502875]), array([-1.17965641, -1.35340529]), array([ 0.68428468, -0.87582345]), array([ 0.79763245, -0.59081494]), array([ 0.39461816, -1.79247245]), array([ 1.18805254, -1.32259356]), array([ 1.37066839, -0.32891523]), array([1.15656705, 0.54921911])]\n",
      "预测结果：\n",
      "[np.int64(0), np.int64(1), np.int64(1), np.int64(0), np.int64(1), np.int64(0), np.int64(1), np.int64(0), np.int64(1), np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(1), np.int64(0), np.int64(1), np.int64(1), np.int64(0), np.int64(0), np.int64(0), np.int64(1), np.int64(1), np.int64(0), np.int64(0), np.int64(0), np.int64(0), np.int64(0), np.int64(0), np.int64(0), np.int64(0)]\n",
      "SSE: 44.00855295550387\n",
      "轮廓系数: 0.2539212412280785\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=3, 初始中心点：[[0.634 0.264]\n",
      " [0.608 0.318]\n",
      " [0.556 0.215]]\n",
      "聚类结果：\n",
      "类别1：[array([ 0.65279919, -0.08242138]), array([ 0.16162552, -0.45986509]), array([ 0.85430633, -1.41502875]), array([ 0.68428468, -0.87582345]), array([ 0.79763245, -0.59081494]), array([ 0.39461816, -1.79247245]), array([ 1.18805254, -1.32259356]), array([ 1.37066839, -0.32891523])]\n",
      "类别2：[array([1.04951638, 1.42735344]), array([1.53439294, 0.78030709]), array([0.48907463, 0.33353699]), array([1.15656705, 0.54921911]), array([-0.32954814,  1.25018599]), array([-0.03358452,  0.72638656]), array([1.38955968, 1.65073849]), array([0.01049516, 1.51978863]), array([-0.36103363,  0.78030709]), array([1.22583513, 1.31180945]), array([-0.53105529,  1.41965051])]\n",
      "类别3：[array([-0.80183051, -0.29040057]), array([-0.31065685, -0.96825865]), array([-0.58772917, -0.49067682]), array([-1.80936623, -0.05931258]), array([-1.79677204, -1.67692846]), array([-1.17965641, -1.35340529]), array([-1.07260574,  0.73408949]), array([-1.07890284, -0.66784427]), array([-1.2048448 , -0.25958883]), array([-1.5637794 , -0.13634191]), array([-0.29806265,  0.28731939])]\n",
      "预测结果：\n",
      "[np.int64(1), np.int64(1), np.int64(0), np.int64(1), np.int64(0), np.int64(2), np.int64(2), np.int64(2), np.int64(0), np.int64(2), np.int64(2), np.int64(2), np.int64(0), np.int64(0), np.int64(2), np.int64(0), np.int64(0), np.int64(2), np.int64(2), np.int64(2), np.int64(0), np.int64(1), np.int64(2), np.int64(1), np.int64(1), np.int64(1), np.int64(1), np.int64(1), np.int64(1), np.int64(1)]\n",
      "SSE: 19.426225670247415\n",
      "轮廓系数: 0.403438205426265\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "K=4, 初始中心点：[[0.403 0.237]\n",
      " [0.481 0.149]\n",
      " [0.437 0.211]\n",
      " [0.666 0.091]]\n",
      "聚类结果：\n",
      "类别1：[array([-1.07260574,  0.73408949]), array([-0.29806265,  0.28731939]), array([-0.32954814,  1.25018599]), array([-0.03358452,  0.72638656]), array([0.01049516, 1.51978863]), array([-0.36103363,  0.78030709]), array([-0.53105529,  1.41965051])]\n",
      "类别2：[array([-0.80183051, -0.29040057]), array([-0.31065685, -0.96825865]), array([-0.58772917, -0.49067682]), array([-1.80936623, -0.05931258]), array([-1.79677204, -1.67692846]), array([-1.17965641, -1.35340529]), array([-1.07890284, -0.66784427]), array([-1.2048448 , -0.25958883]), array([-1.5637794 , -0.13634191])]\n",
      "类别3：[array([1.04951638, 1.42735344]), array([1.53439294, 0.78030709]), array([0.48907463, 0.33353699]), array([1.15656705, 0.54921911]), array([1.38955968, 1.65073849]), array([1.22583513, 1.31180945])]\n",
      "类别4：[array([ 0.65279919, -0.08242138]), array([ 0.16162552, -0.45986509]), array([ 0.85430633, -1.41502875]), array([ 0.68428468, -0.87582345]), array([ 0.79763245, -0.59081494]), array([ 0.39461816, -1.79247245]), array([ 1.18805254, -1.32259356]), array([ 1.37066839, -0.32891523])]\n",
      "预测结果：\n",
      "[np.int64(2), np.int64(2), np.int64(3), np.int64(2), np.int64(3), np.int64(1), np.int64(1), np.int64(1), np.int64(3), np.int64(1), np.int64(1), np.int64(1), np.int64(3), np.int64(3), np.int64(0), np.int64(3), np.int64(3), np.int64(1), np.int64(1), np.int64(1), np.int64(3), np.int64(2), np.int64(0), np.int64(0), np.int64(0), np.int64(2), np.int64(0), np.int64(0), np.int64(2), np.int64(0)]\n",
      "SSE: 12.371240078300698\n",
      "轮廓系数: 0.44632018781637856\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "execution_count": 52
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
